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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimekala, B. Aravind, T. Sounderrajan, T. Rajakumareswaran, V. Kondreddi, Krishnaveni Mohanambal, K. Christ University Department of Computer Science Yeshwanthpur Bengaluru India Vel TecRangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Cse School of Computing India Erode Sengunthar Engineering College Department of Ai and Data Science Tamil Nadu Thuduppathi India Erode Sengunthar Engineering College Department of Computer Science and Design Tamil Nadu Thuduppathi India Chaitanya Bharathi Institute of Technology Electrical and Electronics Engineering Ranga Reddy Telangana Gandipet500075 India Bannariamman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350370249

Cryptographic protocols are used to relax the ever-developing quantity of linked gadgets that make up the net of things (IoT). Those cryptographic protocols have been designed to make certain that IoT tool traffic stays cozy and personal, even while nevertheless allowing tool-to-device and cloud-to-tool communications. Examples of these protocols consist of TLS/SSL, PGP/GPG, IPsec, SSL VPN, and AES encryption. Every one of these protocols enables authentication, message integrity, and confidentiality via encryption and key trade. Moreover, a lot of these protocols are carried out in the form of diverse hardware and software answers, such as smart playing cards and gateways, to make certain that IoT traffic is secured. With the appropriate implementation of those cryptographic protocols, establishments can ensure that their IoT facts are blanketed and securely transmitted. © 2024 IEEE.

关键词： Authentication Communications Cryptographic EverDeveloping Gadgets Protocols

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Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control

Over-the-Air Federated Edge Learning with Error-Feedback One...

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International Conference on Computer and Communications (ICCC)

作者： Yuding Liu Dongzhu Liu Richeng Jin Guangxu Zhu Qingjiang Shi College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Computing Science University of Glasgow Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. The existing one-bit digital over-the-air aggregation scheme features one-bit gradient quantization at edge devices and majority voting-based decoding at edge server, and therefore offers enhanced communication efficiency. However, the low-resolution one-bit gradient quantization slows down the model convergence and leads to performance degradation, while the aggregation errors caused by channel fading in Air-FEEL further impair the performance. To address these issues, we propose the error-feedback one-bit broadband digital aggregation (EFOBDA) with an optimized power control policy. To this end, we first derive the impact of aggregation errors on the convergence of FEEL with EFOBDA. Then, we further introduce a power control policy by maximizing the convergence rate under instantaneous power constraints. The analytical results and optimized power control policy are validated by the experiments, which show that the proposed scheme achieves significantly faster convergence and higher test accuracy in image classification tasks compared with the vanilla one-bit quantization scheme without error feedback or optimized power control policy.

关键词： Performance evaluation Fading channels Training Quantization (signal) Wireless networks Atmospheric modeling Power control Channel estimation Training data Convergence

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ProgMate: An Intelligent Programming Assistant Based on LLM

ProgMate: An Intelligent Programming Assistant Based on LLM

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Frontiers in Education (FIE) Conference

作者： Ying Li Runze Yang Xiantao Zhang Peng Shi Da Yang Xuefei Huang School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like “omnipresence”, “adaptive guidance” and “customization”. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education.

关键词： Adaptive systems Navigation Large language models Digital transformation Education Educational technology Programming profession Monitoring

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Deep Spectral Copula Mechanisms Modeling Coupled and Volatile Multivariate Time Series

Deep Spectral Copula Mechanisms Modeling Coupled and Volatil...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Yang Yang Zhilin Zhao Longbing Cao Data Science Institute University of Technology Sydney Sydney Australia School of Computing Macquarie University Sydney Australia

Exploring inter- and intra-time series relations and handling volatile covariates form various challenges in modeling Coupled and Volatile Multivariate Time Series (CVMTS). A typical CVMTS data is the COVID-19 case time series across multiple countries, whose covariates may involve high volatility caused by missing samples. The existing approaches merely focus on a single set of multivariate time series or multiple multivariate time series without considering their volatile temporal covariates. They do not sufficiently characterize CVMTS features by explicitly modeling intra- and inter-MTS couplings and effectively handling volatile covariates in multiple multivariate time series. Accordingly, we propose Deep Spectral Copula Mechanisms (DSCM) to adapt CVMTS. Specifically, DSCM (1) incorporates a Singular Spectral Analysis (SSA) module to reduce the volatility of multiple covariates; (2) applies an intra-MTS coupling module to explicitly model the temporal couplings within a single set of multivariate time series; and (3) transforms target variables into joint probability distributions by Gaussian copula transformation to establish inter-MTS couplings across multiple multivariate time series. Substantial experiments on COVID-19 time-series data from multiple countries indicate the superiority of DSCM over state-of-the-art approaches.

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AGU2-Net: Multi-Scale U2-Net Enhanced by Attention Gate Mechanism for Image Tampering Localization

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IEEE Access 2025年 13卷 99659-99671页

作者： Kefei Wu Lin Li Qingyan Li School of Science Jimei University Xiamen China Computer Engineering College Jimei University Xiamen China Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei University Xiamen China

The advancement of image editing and compositing technologies has posed significant challenges to the authenticity of digital images. Although deep learning algorithms based on convolutional neural networks (CNNs) have made notable progress in image forgery detection, they still face certain limitations in effectively detecting and localizing tampered areas due to the subtle nature of existing manipulation traces. To address this limitation, we propose AGU2-Net, a novel network architecture that integrates an advanced attention mechanism with multi-scale feature extraction. The architecture employs a nested U-structure as its backbone, enabling effective integration of image features across various scales. Additionally, AGU2-Net is an end-to-end autonomous training network with a lightweight framework that incurs low computational overhead. A significant innovation of the architecture is the introduction of the attention gate mechanism, which enhances the network’s ability to selectively process multi-scale features. By adaptively fusing feature information from the encoder and decoder, this mechanism increases spatial perception and improves the network’s ability to capture intricate local tampering details. Experiments on four publicly available benchmarks show that AGU2-Net achieves top F1 scores of 0.556, 0.338, and 0.273 on the CASIA, NIST16, and IMD2020 datasets, respectively, confirming its superior tamper-detection accuracy and sharper pixel-level localization compared with state-of-the-art methods.

关键词： Feature extraction Computer architecture Location awareness Computational modeling Forgery Attention mechanisms Logic gates Splicing Convolution Accuracy

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Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images

Micro-Objects Classification for Microplastic Pollution Dete...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Naga Sai Munagala Vibhaw Kumar S. Srividhya G. Usha R. Jeyalakshmi Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Chemistry SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Microplastic pollution poses a significant environmental threat, necessitating advanced methods for effective control and mitigation. This paper focuses on the development of a novel approach for micro-objects classification, specifically targeting microplastics, utilizing holographic images. Holography provides a three-dimensional representation of microscopic particles, enhancing the accuracy and efficiency of classification algorithms. In this paper, initially the acquisition of holographic images of water samples containing micro-objects, with a particular emphasis on microplastics are collected. State-of-the-art image processing and machine learning techniques are employed to develop a robust classification system capable of distinguishing between various micro-objects with high precision. Next, the Key objectives include the design and implementation of classification algorithm, leveraging holographic data to differentiate microplastics from other particles. The proposed work aims to enhance the efficiency of microplastic identification, enabling more effective monitoring and control of pollution in aquatic *** outcomes of this paper hold promise for advancing microplastic pollution research and environmental monitoring practices. The integration of holographic images into the classification process offers a unique perspective, providing valuable insights into the characteristics and distribution of microplastics. Ultimately, this research contributes to the development of innovative solutions for addressing the pervasive issue of microplastic pollution.

关键词： Training Accuracy Pollution Image processing Computational modeling Prevention and mitigation Microscopy

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM

Visualizing Program Behavior: A Study of Enhanced Program Di...

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Frontiers in Education (FIE) Conference

作者： Ying Li Runze Yang ShiJie Gui Peng Shi Xuefei Huang Da Yang Xiaozhou Zhang Yiming Gai School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning.

关键词： Location awareness Analytical models Codes Accuracy Statistical analysis Large language models Education data visualization Optimization methods Programming profession

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DEEP ORTHOGONAL HYPERSPHERE COMPRESSION FOR ANOMALY DETECTION

arXiv

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arXiv 2023年

作者： Zhang, Yunhe Sun, Yan Cai, Jinyu Fan, Jicong School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces. In this paper, we first propose a novel deep anomaly detection model that improves the original hypersphere learning through an orthogonal projection layer, which ensures that the training data distribution is consistent with the hypersphere hypothesis, thereby increasing the true positive rate and decreasing the false negative rate. Moreover, we propose a bi-hypersphere compression method to obtain a hyperspherical shell that yields a more compact decision region than a hyperball, which is demonstrated theoretically and numerically. The proposed methods are not confined to common datasets such as image and tabular data, but are also extended to a more challenging but promising scenario, graph-level anomaly detection, which learns graph representation with maximum mutual information between the substructure and global structure features while exploring orthogonal single- or bi-hypersphere anomaly decision boundaries. The numerical and visualization results on benchmark datasets demonstrate the superiority of our methods in comparison to many baselines and state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Rough Set Model and Approximations in Fuzzy Formal Contexts 5

Rough Set Model and Approximations in Fuzzy Formal Contexts

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5th Asia Conference on Machine Learning and computing, ACMLC 2022

作者： Syau, Yu-Ru Lin, En-Bing National Formosa University Department of Information Management Yunlin Huwei63201 Taiwan Wentworth Institute of Technology School of Computing and Data Science BostonMA02132 United States

ISBN: (纸本)9798350333923

Given a set of objects G with a set of attributes M and a binary relation I, a formal context (G, M, I) corresponds to a Boolean-valued information table in rough set theory, and the kernel relation of the function from G to M which maps each object of G to its afterset. This coincides with the indiscernibility relation of the Boolean-valued information table. Rough set model and Variable-Precision model (VP-model) under a formal context can be formulated based on the associated kernel relation on the objects set. α-fuzzified rough set model and VP-model under a fuzzy formal context are formulated analogously. We present an example to demonstrate the proposed α-fuzzified rough set model and VP-model under a fuzzy formal context. © 2022 IEEE.

关键词： Rough set theory

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Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review

arXiv

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arXiv 2024年

作者： Chan, Yi Hao Girish, Deepank Gupta, Sukrit Xia, Jing Kasi, Chockalingam He, Yinan Wang, Conghao Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Computer Science and Engineering Indian Institute of Technology Punjab Ropar140001 India

Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. However, existing methods of evaluating their robustness are often limited to cross-referencing with existing literature, which is a subjective and inconsistent process. In this review, we provide an overview of how GNN and model explainability techniques (specifically, feature attributors) have been applied to fMRI datasets for disorder prediction tasks, with an emphasis on evaluating the robustness of potential biomarkers produced for psychiatric disorders. Then, 65 studies using GNNs that reported potential fMRI biomarkers for psychiatric disorders (attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia) published before 9 October 2024 were identified from 2 online databases (Scopus, PubMed). We found that while most studies have performant models, salient features highlighted in these studies (as determined by feature attribution scores) vary greatly across studies on the same disorder. Reproducibility of biomarkers is only limited to a small subset at the level of regions and few transdiagnostic biomarkers were identified. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on discovering robust biomarkers of psychiatric disorders via GNNs. © 2024, CC BY-SA.

关键词： Graph neural networks

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